Sorry, but I can't let this statement go past. The go programs in the 90s did local search, but not much global search. For example Many Faces did a one ply global search, with a variable depth quiescence search. I added an alpha-beta search to Many Faces last year, and it made a huge improvement in strength. So it is not true that alpha-beta pruning hit a roadblock.
For me, the big advantage of UCT/MC is that it eliminates the huge, slow, buggy evaluation function. Simple playouts are much much easier to make bug free. Bugs in the evaluation function caused many losses, and are almost impossible to eliminate in traditional programs, since the evaluation functions are so complex. David It seems that alpha/beta pruning hit a roadblock a long time ago in go. Now we have MC... which as you increase the number of samples.. you start to get closer to an equivalent alpha/beta. But... there are still huge groups of samples that are not checked... and if you want to somehow prove you have the best move... how will you do it? Will you make the sample size equivalent to the number of possible samples? How will you do this practically? You can only state with a certain confidence that you did make the best move and this would be bound by our computational resources.
_______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
